Research On Pedestrian Position Estimation Methods Based On One Node Sensor Data Fusion

Accurate person position estimation is critical in applications such as indoor and outdoor pedestrian navigation,human target tracking,and behavior monitoring.Especially in the emergency rescue scene with complicated scenes,in a high-rise building,a disaster scene with poor visibility or a crowded place,to realize a long-term stable and high-precision three-dimensional position estimation of inside and outside personnel,relying on a single portable sensing device to environment without relying on external infrastructure,has become a key issue in the field of positioning research.A self-developed integrated sensor is used to estimate the position of pedestrians.The device is mainly composed of a micro-IMU and a barometer.A single sensor node is formed by wearing a sensor device on the ankle to collect information about the speed,direction and height change of the personnel when walking.On the basis of summarizing the current research contents at home and abroad,this paper proposes a pedestrian position estimation method based on single node sensor data fusion.The main research contents are as follows:The 3D-space pedestrian position estimation includes the height position estimation and the horizontal 2D position estimation.First,the gait stance phase and swinging phase were determined based on the multi-threshold gait phase detection method to realize the step,heading angle,speed and position parameter extraction of pedestrian extraction of the pedestrian swing phase.In order to minimize IMU drift error,the vertical acceleration and velocity in the stance phase are set to zero.Secondly,by using the acceleration and angular velocity as well as the height derived from the barometer data,the high-precision altitude position estimation of the pedestrian was obtained.A complementary filter and a target error compensation algorithm are used to reduce the cumulative error in the height estimation caused by the IMU inherent drift.Finally,based on the pedestrian trajectory estimation mechanism,the multi-modal step estimation model is built to complete the stride length estimation,and the heading angle estimation is performed based on the data fusion of the multiplicative extended Kalman filter to realize the pedestrian horizontal 2D position estimation.The experimental results show that the ratio of the estimation error of the horizontal position to the total walking distance is about 2%,and the estimation error of the height position accounts for about 2%of the total height in the long-distance multi-story buildings and the complex indoor and outdoor scenes,which achieves the overall highprecision and stable three-dimensional position estimation of pedestrians.The proposed pedestrian position estimating device and method can overcome single sensor drift error,have good long-time stability and environmental adaptability,and can realize highprecision indoor and outdoor pedestrian position estimation,and is widely applicable to applications such as personnel positioning navigation.